Zobrazeno 1 - 10
of 75
pro vyhledávání: '"FASHENG WANG"'
Unscented Particle Filter for Online Total Image Jacobian Matrix Estimation in Robot Visual Servoing
Publikováno v:
IEEE Access, Vol 7, Pp 92020-92029 (2019)
The main purpose of visual servoing is to control the motion of a robot system based on visual information provided by one or more cameras. It is an important research topic in the robotics community. In uncalibrated visual servoing, the image Jacobi
Externí odkaz:
https://doaj.org/article/6df9cc3d8d7c4e268a411b5daaa556a7
Publikováno v:
IEEE Access, Vol 6, Pp 13803-13809 (2018)
The past several decades have witnessed the successful application of sequential Monte Carlo method (or particle filter) to a variety of fields. It has grown to be a popular method in solving different kinds of nonlinear Bayesian estimation problems.
Externí odkaz:
https://doaj.org/article/2b6ee1f723324a149ebd81636e4d73bc
Autor:
Fasheng Wang, Tianyi David Luo, Chunyong Chen, Yun Xie, Zhangxiong Lin, Da Zeng, Jianhua Lin, Junjian Ye
Publikováno v:
Journal of Orthopaedic Surgery, Vol 27 (2019)
Purpose: The purpose of this study was to assess the outcomes in a series of patients, who underwent cerclage and figure-of-eight tension band wiring using a single titanium cable for comminuted patellar fractures. Methods: We describe a modified ten
Externí odkaz:
https://doaj.org/article/4ec513fd6a1c45c3af80302e63f85faa
Publikováno v:
Information, Vol 11, Iss 4, p 214 (2020)
The particle filter method is a basic tool for inference on nonlinear partially observed Markov process models. Recently, it has been applied to solve constrained nonlinear filtering problems. Incorporating constraints could improve the state estimat
Externí odkaz:
https://doaj.org/article/e1de4e1f5c574af18ff5b2078c335450
Publikováno v:
Multimedia Systems. 29:1131-1144
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b7ff31c0b39f8e98ea87c083d26b950e
https://doi.org/10.1007/s00530-022-01043-0
https://doi.org/10.1007/s00530-022-01043-0
Publikováno v:
International Journal of Computer Vision. 131:899-917
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::858aa8c4519bbce870b9e1f2a35cd259
https://doi.org/10.1007/s11263-022-01732-3
https://doi.org/10.1007/s11263-022-01732-3
Publikováno v:
ACM Transactions on Multimedia Computing, Communications & Applications; Mar2024, Vol. 20 Issue 3, p1-22, 22p
Autor:
Dandan Huang, Siyu Yu, Jin Duan, Yingzhi Wang, Anni Yao, Yiwen Wang, Junhan Xi, Fasheng Wang, Guohui Wang
Publikováno v:
Frontiers in Physics; 2023, p01-14, 14p
Publikováno v:
Multimedia Tools and Applications. 81:27879-27893
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::9fb3d97c7d5d804b2d29c5f44961fcad
https://doi.org/10.1007/s11042-022-12760-z
https://doi.org/10.1007/s11042-022-12760-z
To date, existing Siamese-based trackers have achieved excellent performance. However, in some complex scenarios, using deep convolutional layers alone can not effectively capture powerful representative features. To solve this problem, we propose a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c441166e9744fb8ecf2089a4c5d75c15
https://doi.org/10.21203/rs.3.rs-2190588/v1
https://doi.org/10.21203/rs.3.rs-2190588/v1
